Laminar

Laminar is an open-source observability platform purpose-built for AI agents.

• Tracing. Docs
  • OpenTelemetry-native powerful tracing SDK - 1 line of code to automatically trace Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, Gemini, and more.
• Signals. Docs
  • Describe any behavior of your agent that you want to track in plain English (e.g. "agent is stuck in a loop")
  • Laminar reads every agent run and pings you in Slack when it happens.
• Evals. Docs
  • Unopinionated, extensible SDK and CLI for running evals locally or in CI/CD pipeline.
  • UI for visualizing evals and comparing results.
• MCP / CLI access for your coding agent
  • Query traces, spans, metrics, and events with SQL
  • Let your coding agent investigate and debug issues based on your traces
• Dashboards. Docs
  • Powerful dashboard builder for traces, metrics, and events with support of custom SQL queries.
• Data annotation & Datasets. Docs
  • Custom data rendering UI for fast data annotation and dataset creation for evals.
• Extremely high performance.
  • Written in Rust 🦀
  • 20x trace compression for efficient ingestion and storage. Read more about it here.
  • Custom realtime engine for viewing traces as they happen.
  • Ultra-fast full-text search over span data.
  • gRPC exporter for tracing data.

Documentation

Check out the full documentation here laminar.sh/docs.

Getting started

The fastest and easiest way to get started is with our managed platform -> laminar.sh

Self-hosting with Docker compose

Laminar is very easy to self-host locally. For a quick start, clone the repo and start the services with docker compose:

git clone https://github.com/lmnr-ai/lmnr
cd lmnr
docker compose up -d

This will spin up a lightweight but full-featured version of the stack. This is good for a quickstart or for lightweight usage. You can access the UI at http://localhost:5667 in your browser.

You will also need to properly configure the SDK, with baseUrl and correct ports. See guide on self-hosting.

For production environment, we recommend using our managed platform or docker compose -f docker-compose-full.yml up -d.

Configuring LLM provider (optional)

Frontend AI features (chat-with-trace, SQL-with-AI) and server-side AI workers require an LLM provider. Configure one in your .env file at the repo root.

Pick one of the following provider setups. LLM_MODEL_SMALL|MEDIUM|LARGE are optional — per-provider defaults apply when unset. LLM_DEFAULT_HEADERS_JSON is optional for any provider or gateway that requires static headers.

# Optional for any provider/gateway that requires static headers
# LLM_DEFAULT_HEADERS_JSON='{"X-Gateway-Tenant":"tenant"}'

# Option A: Gemini
LLM_PROVIDER=gemini
LLM_API_KEY=your_gemini_key

# Option B: OpenAI (or any OpenAI-compatible gateway such as LiteLLM, OpenRouter, vLLM)
LLM_PROVIDER=openai
# LLM_BASE_URL=http://localhost:4000   # optional, for OpenAI-compatible gateways
LLM_API_KEY=your_openai_key

# Option C: AWS Bedrock (Anthropic Claude). Uses AWS credentials instead of LLM_API_KEY